Some illuminating remarks on molecular genetics and genomics as well as drug development

Chou, Kuo‐Chen

doi:10.1007/s00438-019-01634-z

Cited by 6 publications

(3 citation statements)

References 266 publications

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“…In computational biology, an important but challenging step is how to represent a biological sequence by a discrete model or a vector that captures its key features without losing sequence-order information, since most of the existing machine-learning algorithms can only handle fixed-length numerical vectors [ 86 ], [ 87 ]. When dealing with protein sequences, the simplest way to characterize a protein by a fixed-length numerical vector is to extract the information of the protein sequence from the entire amino acid sequence, especially when the protein does not have significant homology to annotated proteins.…”

Section: Methodsmentioning

confidence: 99%

Use of Chou’s 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

Bouziane

Chouarfia

2020

Journal of Integrative Bioinformatics

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AbstractTo date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein–protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.

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Section: Methodsmentioning

confidence: 99%

Use of Chou’s 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

Bouziane

Chouarfia

2020

Journal of Integrative Bioinformatics

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“…For the wonderful and awesome roles of the "5-steps rule" in driving proteome, genome analyses and drug development, see a series of recent papers [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] where the rule and its wide applications have been very impressively presented from various aspects or at different angles.…”

Section: Predictormentioning

confidence: 99%

The Ploc Bal-Manimal is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Animal Proteins Based on their Sequence Information Alone

Chou

2020

BPOJ

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“…To cope with the "data explosion" and solve complex biological problems, medical scientists, biologists, and pharmacists are constantly seeking help from mathematics and computer experts. As a result, a number of disciplines based on computing and simulation, such as bioinformatics, computational biology, pharmaceutical informatics, systems biology, network pharmacology and systems pharmacology, quantitative pharmacology and quantitative system pharmacology (QSP) came into being [3,4] .…”

Section: Introductionmentioning

confidence: 99%

Application value of quantitative system pharmacology in drug discovery for traditional Chinese medicine

Meng

Tao

2020

mcrr

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Quantitative system pharmacology (QSP) is a discipline that combines computational models of systems biology and systems pharmacology. With the development of high-throughput genomics techniques (genomics, transcriptomics, proteomics, and metabolomics) as well as computer and bioinformatics methods, systems biology and systems pharmacology modeling are widely used to comprehend human biology and disease progression, predict the effectiveness and safety of drug candidates. Due to the advancement of big data and high-quality database, the application of QSP, especially the pre-clinical stage that guides early drug discovery, is increasingly widespread. The traditional drug discovery process takes a long time yet has a low success rate. The early intervention and full participation of QSP in the development of new drugs discovery can form a model-led drug development model to improve the efficiency of drug discovery and scientific appraise, reduce the cost of research and development, and shorten the time to market for new drugs. This article reviews the differences between QSP and other quantitative pharmacology, the problems faced by traditional Chinese medicine research, and the value of QSP in traditional Chinese medicine research, with a view to providing reference and support for the research and development of new traditional Chinese medicine.

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Some illuminating remarks on molecular genetics and genomics as well as drug development

Cited by 6 publications

References 266 publications

Use of Chou’s 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

Use of Chou’s 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

The Ploc Bal-Manimal is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Animal Proteins Based on their Sequence Information Alone

Application value of quantitative system pharmacology in drug discovery for traditional Chinese medicine

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